Strategies to Help Students Tackle Complicated Anatomical Terminology
Notice bibliographique
Résumé
The study of anatomy involves the acquisition of a new language as students familiarize themselves with the names of the components of organs and organ systems. Many of these names derive from Latin and Greek, languages with which students have limited experience and they struggle with the pronunciation and spelling of each new term. Furthermore, students gain little experience in actively recalling, speaking and writing this terminology (they are provided with PowerPoint (PPT) lecture slides) and high enrolments (250–300 students per course) make weekly laboratory sessions impossible. Interactive learning strategies are needed to enrich their educational experience. Videos narrated by peers allow students to hear the names of bones and muscles while simultaneously using visual pathways to situate their study of musculoskeletal (MSK) anatomy within applied contexts such as a fractured wrist or the assumption of a yoga pose. Interactive PPT‐based Hangman games (PPTAlchemy R ) require students to both recall functional anatomy content and to focus on the letters composing a muscle's name when assigning letters to spaces. Indeed, gamification is a new educational trend that promotes interactive learning by giving students low‐stakes opportunities to practice and to learn from their errors. A final approach, the development of content‐specific crossword puzzles using EclipseCrossword R , also uses clues to stimulate recall and forces students to focus on word structure. But these puzzles have the additional advantages of accommodating words of unlimited length (word limit for PPT‐based Hangman is 15 letters), alerting students to an answer with the wrong number of letters, allowing students to identify spelling errors following puzzle submission, and motivating students to produce a finished product. Despite the fact that marks were not assigned to these interactive activities, tracking data shows that they were well used by students when preparing for summative evaluation. Students in two undergraduate courses in MSK anatomy (n = 233 and 235) presented with a series of four yoga videos over the three weeks leading up to summative exams watched each video an average of 1.92–2.70 times with most students being sufficiently engaged to view the entire series at least once. Preliminary data tracking student use of crossword puzzles in a first‐year anatomy and physiology course (n = 296) revealed a high level of interest when each puzzle was released (immediately accessed by over 25% of students) and that many students (at least 25–30%) returned to try the puzzles more than once. These studies involving students of anatomy show that educational strategies that combine audio with visual and/or use interactive word‐based games can enrich learning and are welcomed by students tackling a new language. Support or Funding Information University of Ottawa Undergraduate Research Opportunity Program
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,043 | 0,028 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».